Jason M. Klusowski

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共著者

Andrew R BarronProfessor of Statistics, Yale University確認したメールアドレス: yale.edu
Dana YangAssistant Professor, Cornell University確認したメールアドレス: cornell.edu
Weijie SuAssociate Professor, University of Pennsylvania確認したメールアドレス: wharton.upenn.edu
Cynthia RushColumbia University確認したメールアドレス: columbia.edu
Zhiqi BuAmazon AI確認したメールアドレス: sas.upenn.edu
Matias D. CattaneoPrinceton University確認したメールアドレス: princeton.edu
Yihong WuProfessor, Department of Statistics and Data Science確認したメールアドレス: yale.edu
Ryan TheisenUniversity of California, Berkeley確認したメールアドレス: berkeley.edu
Victor-Emmanuel BrunelENSAE-CREST, Professor in Statistics確認したメールアドレス: ensae.fr
Michael MahoneyProfessor of Statistics, UC Berkeley確認したメールアドレス: stat.berkeley.edu
Rajita ChandakPrinceton University確認したメールアドレス: princeton.edu
Boris ShigidaPhD Student, Princeton University確認したメールアドレス: princeton.edu
Caiming XiongSalesforce Research確認したメールアドレス: salesforce.com
Nitish Shirish KeskarOpenAI確認したメールアドレス: openai.com
Huan WangSalesforce Research確認したメールアドレス: yale.edu
Richard Socheryou.com確認したメールアドレス: stanford.edu
William G UnderwoodPhD Candidate, ORFE Department, Princeton University確認したメールアドレス: princeton.edu
Xin ChenPrinceton University確認したメールアドレス: princeton.edu
Jonathan W. SiegelAssistant Professor, Texas A&M University確認したメールアドレス: tamu.edu
Yan Shuo TanAssistant Professor in Statistics, National University of Singapore確認したメールアドレス: berkeley.edu

フォロー

Jason M. Klusowski

Assistant Professor, Department of Operations Research & Financial Engineering

確認したメールアドレス: princeton.edu - ホームページ

statistics probability machine learning information theory


タイトル引用回数順公開年順タイトル順	引用先引用先	年
Approximation by Combinations of ReLU and Squared ReLU Ridge Functions With and Controls JM Klusowski, AR Barron IEEE Transactions on Information Theory 64 (12), 7649-7656, 2018	160*	2018
Approximation and estimation for high-dimensional deep learning networks AR Barron, JM Klusowski arXiv preprint arXiv:1809.03090, 2018	67	2018
Risk bounds for high-dimensional ridge function combinations including neural networks JM Klusowski, AR Barron arXiv preprint arXiv:1607.01434, 2016	67	2016
Algorithmic analysis and statistical estimation of SLOPE via approximate message passing Z Bu, JM Klusowski, C Rush, WJ Su IEEE Transactions on Information Theory 67 (1), 506-537, 2020	53	2020
Sharp analysis of a simple model for random forests J Klusowski International Conference on Artificial Intelligence and Statistics, 757-765, 2021	52*	2021
Estimating the coefficients of a mixture of two linear regressions by expectation maximization JM Klusowski, D Yang, WD Brinda IEEE Transactions on Information Theory 65 (6), 3515-3524, 2019	51	2019
Large scale prediction with decision trees JM Klusowski, PM Tian Journal of the American Statistical Association 119 (545), 525-537, 2024	29*	2024
Complexity, statistical risk, and metric entropy of deep nets using total path variation AR Barron, JM Klusowski arXiv preprint arXiv:1902.00800, 2019	27	2019
Counting motifs with graph sampling JM Klusowski, Y Wu Conference On Learning Theory, 1966-2011, 2018	27	2018
Statistical guarantees for estimating the centers of a two-component Gaussian mixture by EM JM Klusowski, WD Brinda arXiv preprint arXiv:1608.02280, 2016	22	2016
Sparse learning with CART J Klusowski Advances in Neural Information Processing Systems 33, 11612-11622, 2020	19	2020
Minimax lower bounds for ridge combinations including neural nets JM Klusowski, AR Barron 2017 IEEE International Symposium on Information Theory (ISIT), 1376-1380, 2017	18	2017
Estimating the number of connected components in a graph via subgraph sampling JM Klusowski, Y Wu	15	2020
Characterizing the SLOPE trade-off: A variational perspective and the Donoho–Tanner limit Z Bu, JM Klusowski, C Rush, WJ Su The Annals of Statistics 51 (1), 33-61, 2023	10	2023
Analyzing cart JM Klusowski arXiv preprint arXiv:1906.10086, 2019	8	2019
On the implicit bias of adam MD Cattaneo, JM Klusowski, B Shigida arXiv preprint arXiv:2309.00079, 2023	7	2023
Estimation of convex supports from noisy measurements VE Brunel, JM Klusowski, D Yang	7	2021
Good classifiers are abundant in the interpolating regime R Theisen, J Klusowski, M Mahoney International Conference on Artificial Intelligence and Statistics, 3376-3384, 2021	7*	2021
Convergence rates of oblique regression trees for flexible function libraries MD Cattaneo, R Chandak, JM Klusowski arXiv preprint arXiv:2210.14429, 2022	5	2022
Nonparametric variable screening with optimal decision stumps J Klusowski, P Tian International Conference on Artificial Intelligence and Statistics, 748-756, 2021	5	2021

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